Data science emerged in psychiatry some years ago, representing all the digital information about mental health, individuals’ properties, and digital factors involved in the health care processes. It ranges from theoretical variables of interest such as major life events, comorbid diagnosis, and stress factors to blood marker levels and clinical characteristics to functional neuroimaging [21]. DP gives HCPs a new set of digital biomarkers, collected from wearables, smartphones, but also virtual reality devices [22] or in gaming contexts [23], offering the opportunity to model mental health [24] and the extent of individual-environment interactions.

Machine learning brings powerful tools to explore high-dimensional and real-time data concerned with DP. It offers the opportunity “to make sense” of these digital signs of the reality they try to represent in the state of mental health [25-27]. Some see in this technology the potential to better understand the neurobiological mechanisms underlying psychiatric illnesses [28] or give new transdiagnostic models of comprehension of symptoms, in line with the “RDoC” perspective [29]. In addition, ML could be used to deliver new predictive models. Artificial intelligence (AI) might be capable of “overcoming the trial and error-driven status quo in mental health care by supporting precise diagnoses, prognosis, and therapeutic choices” [30]. These systems may have the ability to predict risks, help HCPs make clinical decisions, increase the accuracy and speed of diagnosis, and facilitate the examination of health records. Integrating this data-driven approach into clinical practice could reduce the workload of HCPs. They can already efficiently perform tasks such as diagnosing skin diseases and analyzing medical images (eg, in neurology, ophthalmology, cardiology, and gastro-enterology) [31,32] and could soon be used in clinical decision support systems in psychiatry [33], making compulsory admissions more helpful [34]. DP therefore has a natural home in psychiatry [35,36], helping HCPs access a new set of data based on individuals’ behavioral experiences and increasing their ability to classify and understand symptoms in their contextual and temporal dimensions, as well as the illnesses themselves [37,38]. Data reflecting emotions, levels of energy, behavioral changes [39], symptoms such as sociability, mood, physical activity, and sleep [40], but also logorrhea, agitation, rumination, hallucinations, or suicidal thoughts, could constitute the digital signature of a pathology (Figure 1). Given that psychiatry usually explains a mental illness and its issues in terms of the difficulty patients have interacting with their environment, it is only logical for DP to arouse such interest, especially as it could compensate for the absence of reliable biomarkers.

In recent decades, the psychometric assessment of patients has involved self-report questionnaires (eg, the Patient Health Questionnaire for Depression [41]) and observer-rated scales (eg, the Hamilton Rating Scale for Depression [42]). As these scales are filled in either by the patient or the HCP, they necessarily have a degree of subjectivity. DP would enable phenomenological data to be collected, with the possibility of establishing ecologically valid psychometric assessments [37,43] (Figure 1). For instance, user-generated content on social media sites such as Reddit allows for the recognition of mental illness-related posts with good accuracy using a deep learning approach [44].

Various studies have already provided evidence about the use of DP for diagnostic purposes. As an example, there is a correlation between circadian rhythm, step counts, or heart rate variability and the diagnosis of a mood disorder or mood episode [45-48]. Other correlations have been found between data and symptoms of schizophrenia [49-51], major depression [52-57], mood disorders [46,58,59], posttraumatic stress disorder [60,61], generalized anxiety disorder [62], suicidal thoughts [63,64], sleep disorders [65], addiction [66], stress [53,67], postpartum [68,69], autism [70], and child and adolescent psychiatry [71,72]. Among other examples of the efficiency of DP for prediction or diagnosis in mental health, Instagram photos or Facebook language have been found to be predictors of depression [73,74]; suicidal risk could be assessed from social media [75,76] with increasing precision if DP would integrate electronic health records data [77,78]; automated analysis of free speech can measure relevant mental health changes in emergent psychosis [79] or incoherence in speech in schizophrenia [80]. It is worth noting that clinical utility may be derived from a combination of passive and active data and not from passive data alone [81]. In total, increasing diagnostic accuracy could help avoid treatment delays or errors. For example, there is currently an average 8-year delay in the diagnosis of bipolar illness in France [82]. Concerning the choice of medication, phenotypic markers already play a major role (eg, antidepressants for depression, whether it is accompanied by insomnia or hypersomnia). DP could bring together more phenotypic markers than HCPs can collect and thus improve the choice of treatment, countering the cognitive bias effect on decision-making [83].

Finally, DP could improve the follow-up of people with serious mental illnesses and optimize care between 2 consultations, especially when accessing care facilities can be difficult. It could also improve the assessment of treatment efficiency. Some researchers have hypothesized that heart rate variability is influenced by the progression of the patient’s depression and could be a new biomarker of treatment response [84,85]. High diagnostic confidence improves treatment compliance, and problems with compliance are a frequent source of relapse in mental illness [86]. DP could in turn provide useful tools for enhancing therapeutic education and improving the prediction of relapses [48,87-90], Ultimately, DP could also be used to assess whether patients could benefit from psychotherapy [91], using parameters with proven efficiency [45], as part of an evidence-based medicine strategy.

DP introduces new capacities to assess differences between what is normal and what is pathological. Canguilhem [92] identified normative capacity (ie, normativity) as a central condition for gauging to what extent a living person is in good health: “What characterizes health is the possibility to tolerate infractions of the usual norm and to set new norms for new situations.” Thus, all living beings live with their own norms based on their specific biological limits. These norms are defined by Canguilhem [92] as a mode of functioning in certain environmental conditions that allows individuals to have normal abilities and live normal lives. Unlike machines, living beings have the possibility of defining their norms according to environmental conditions in order to ensure real-time adaptation. The limits of these norms are inevitably tested in the course of the individuals’ biological lives as they interact with their environment and pursue their life goals, and when they have a disability that prevents them from meeting their goals, they feel ill. Nonetheless, this experience of the limits is based on self-feeling and is totally personal. When this normative capacity no longer allows an individual to adapt, thus triggering an illness, it is legitimate to intervene in order to restore the ability to set new life goals. Concerning psychiatric symptoms, DP could add a digital dimension to the notion of limits and the production and assessment of norms. Experiencing physical pain when one’s leg is broken is more readily acknowledged, and seeking help is a common response. However, people often encounter challenges in recognizing psychic pain [93], depressive symptoms, or anxiety and find it much more difficult to perceive these conditions as abnormal and deserving of appropriate adaptations or treatment. DP could provide a novel and sensitive approach that empowers individuals to define their own digital mental health and establish their own digital norms. In accordance with Canguilhem’s principles, the threshold between the normal and the pathological would become more nuanced [92,94]. Individuals would be able to perceive mental health as something more tangible, allowing them to determine for themselves which conditions contribute to their well-being independently of any scientific paradigms used by HCPs for their evaluation and treatment approaches. Consequently, individuals would have the opportunity to take charge of their mental health care, paving the way for a comprehensive mental health prevention system. With this perspective, it would be simpler to harness individual motivation for making changes and foster active participation in health strategies. In the United States, data-driven psychiatry is starting to emerge, with a close relationship among patients, HCPs, and DP [95]. There have been attempts at self-management, where patients are given greater autonomy with regard to technologies and the management of their symptoms [94]. For instance, some open science applications such as mindLAMP (which collects health data, produces easy-to-understand graphs, enables journaling thoughts and reflections, and offers customized mental health interventions) allow some depressed or alcoholic patients to rapidly develop emotional self-awareness, track thinking patterns in real time, gain insight into their progress, connect to the clinical team, engage with medication, or reinforce their confidence in psychotherapy [96]. Other studies have suggested that DP increases patients’ feelings of control over their symptoms [97]. The quantified self-approach could help scientists understand pathology better and deserves further exploration. In Western medicine, representations of diseases and health are biased by cultural interpretation. Thanks to the new language through the data concerning the self-evaluation close to a data-feeling, patients could free themselves from this bias. Herein lies the idea of compensating for the subjectivity of clinical interviews. DP could introduce a more precise and decisive point of view of the daily life of the patient, where functional impairments, one of the major characteristics of mental illness, must not be neglected. DP is therefore a potential source of objectivity in mental illnesses [98], making it possible to flag up daily abnormalities.

The possibility of empowerment is consistent with psychiatry’s move toward a more personalized approach. It was initially conceived of as the tailoring of psychiatric practices to the patient’s situation based on the HCP’s assessment. However, it could take the form of personalized requests from patients to HCPs, with better comprehension of their mental disorders converting patients into self-researchers investigating their own illness. DP could create a unique network of macro (social and smart cities), meso (relation to environment and situations), micro (the person), and very micro (physiological mechanisms) data integrated into the patient’s daily life. DP would promote personalized psychiatry [99], doing away with the usual lengthy periods of observation in psychiatry. This corresponds to the concept of intelligent health, defined as the expansion of electronic health through the inclusion of built-in data analysis using new technologies, as well as the extension of patient assessment to the patient’s environment and HCPs, and data mining to support decision-making [100]. HCPs and patients must be able to select the parameters that match their characteristics and situation in a move toward clinical augmentation. This added precision in clinical observation should lead to equally shared (between HCPs and patients) models of illnesses and treatments. It opens the way to patient-centered mental health, where “the symptom network” paradigm takes its place for a better phenotypic characterization of disorders and their evolution. The symptom network aims at a technologically augmented clinical and therapeutic relationship [101] that could thus explore how symptoms influence each other without trying to find a unique causal source in a more consistent and transparent psychopathological framework [102].

To conclude, we can see how DP could bring about major scientific progress in psychiatry toward patient-centered mental health. Nevertheless, this optimistic view of its potential uses or advantages needs to be tempered by practical issues. This quest for optimized performance could also have negative impacts (Textbox 1) on society that still need to be assessed.

Although DP introduces the possibility of individuals managing their own health data, increasing the amount of information available for them to assess their own health status, there might also be a process of reduction or simplification that would have massive consequences for patients’ self-determination. Moreover, as their health data would be more readily available to third parties, it might end up being used for other purposes besides improving their health.

The clinical interview is the moment when patients’ complaints are heard by HCPs and their distress is recognized. Their illness is discussed and diagnosed, and an appropriate treatment is prescribed. Subjective representations of individuals and HCPs are part of the decision-making system in psychiatry, shaping discussions about the individual's state of health and illness. In striving to improve the current state of health, a shared objective guides the HCPs’ decisions. However, considering the data yielded by DP analysis, the decision-making process in psychiatry might undergo profound transformations to align with the evolving understanding of mental health. Canguilhem [92] explained that the risk of nominalism is to reduce life to machine functioning. For example, a healthy individual may start to feel ill after seeing figures that deviate from the norm, while a help-seeker may be declared healthy by DP. The patient’s status may thus become disconnected from the felt experience [103], excluding self-construal, illness narratives, interpersonal dynamics, and social contexts, which are determinants of mental health [18]. The term data-driven (in society, marketing, health, etc) already exists and is used when decision-making is mainly based on data interpretation. The question of who creates the norms and indicators does not seem to be an issue at present. By default, these conditions are fixed either by researchers on the basis of their study findings or by the designers of health applications (eg, walking 10,000 steps per day, limiting screen time to 4 hours per day, and eating certain types of food selected by an algorithm). For example, a marketing campaign by Yamasa Corporation claimed that walking 10,000 steps per day is a factor for well-being. However, the goal was to promote a step tracker during the 1964 Tokyo Olympics. They called this technology manpo-kei, which in Japanese literally means “10,000 steps counter.” A systematic review acknowledged the association between walking and the reduction of all causes of mortality [104] but concluded that we can expect more benefits with every 1000 steps we walk beyond 8000. The 10,000-step target is therefore arbitrary and should be reconsidered. This example helps us understand that many recent applications continue to rely on approximations of scientific data [105,106]. At present, too many applications are dedicated to different types of data collection and analysis methods that are not well assimilated [40,107]. Using norms set by DP could deprive individuals of the possibility of discussing which norms they should strive toward in order to achieve better well-being. Studies on DP in psychiatry have limited research interest [108], none of which has been applied to clinical diagnosis and treatment or shown improvements in mental health over the long term. Some applications, such as Instagram, are already suspected of damaging users’ mental health [109].

Froment [103] pointed out that historically speaking, the term illness is not technical but undeniably profane and phenomenological. The science of medicine was built on a prescientific conception of illnesses but has gradually adopted concepts developed by HCPs and health authorities. If DP modifies these concepts, it could compromise patients’ quest to understand their mental health as well as the aim of the treatment. Beyond the very definition of mental health, there is an issue with the power of data over people’s freedom and way of life in society. It compromises the very possibility of innovative objectivity or may lead to the outright failure of any attempt to achieve objectivity through this technology.

Datafication, which is defined as the tendency to overrepresent objects with digital data (eg, DP in psychiatry), has several detractors, especially when it comes to the possibility of data representing part of the real world [93,110-112]. For instance, several scales have been developed to quantify psychic pain, but they are very heterogeneous in terms of content, and cannot be compared or replaced by each other [112]. Data power may therefore paradoxically deplete reality [113]. Each of the many steps between data collection, analysis by an algorithm, and implementation by patients or HCPs is a source of bias [114-119]. Algorithmic bias emerges when databases are created without exhaustive data or when data is sourced from patients with coexisting illnesses [120,121]. Additionally, machine learning algorithms give rise to the black box effect (ie, the opacity of the internal processes of a system that produce outputs from inputs) [122]. This concern appears when users (eg, HCPs or patients) lack knowledge about the inner workings of the system, rendering them unable to explain the outcomes of an analysis [123]. Studies exploring AI-based diagnosis do not take either difficult cases or HCPs’ clinical experience into account. AI makes mistakes when HCPs perform poorly [124]. Machine learning searches for statistical invariants, whereas clinical experience provides tacit data (ie, heuristics coupled with models or concepts, used unconsciously by HCPs, and which are complex to objectify) that algorithms have difficulty considering, although they are at the core of clinical practice in psychiatry. Objectivity is a cultural and historical construct that has elicited different approaches over time and across cultures [125]. There has been a tendency to want to quantify illnesses ever since Claude Bernard introduced the nominalist approach denounced by Canguilhem [92]. Some see self-monitoring as the instrumentalization of health, promoting the proliferation of digital tracking tools with spurious ethical claims [126]. Even more so than other specialties, psychiatry may see itself as alienated from so-called algorithmic governmentality and, paradoxically, adopt the fantasy of illness-centered medicine curing psychiatric illnesses. Algorithmic governmentality does away with direct human interaction. Rouvroy and Stiegler [112] and Rouvroy and Berns [127] introduced this notion, defining it as derivative, with norm production reliant on massive data sets and regulations favored over humans’ anticipatory ability. With DP, psychiatry could therefore end up being driven by unreflective and determined rules instead of by the desire to create greater freedom to achieve goals. The risk is that it depends less on the HCP’s experience and allows less space for patients to express themselves [128]. DP may also lead us to a hostile world, driven by financial interests, where mental health becomes an unregulated market [129]. In addition, there is a risk of psychiatry becoming purely defensive [130], where medical tests or coercion are used under legal constraints. Finally, although DP represents an opportunity for patients to express themselves through technologies, it may bring more anxiogenic modalities of communication and hinder relationships [113], whereas they are actually crucial for patient mental health care.

The opportunity for social and human progress in patients’ empowerment in psychiatry remains unclear. DP represents an instrument of biopower (derived from Foucault’s biopower [131,132]) for users, independently of its efficiency. The growing consideration of digital data in decision-making risks dispossessing humans (HCPs and patients) of the tools for producing health data. It may create a dependence on production tools, as already observed in the world of scientific research. Stiegler [133] demonstrated how researchers’ daily lives have been changed by the arrival of more and more digital interfaces between the objects they observe and the data required to produce knowledge. Stiegler [133] claimed that scientists are thus deprived of their production as they have to pay private industry for the right to use their own discoveries. DP could easily spark a similar process in the mental health industry [134], where the production of feelings becomes a patentable technique, introducing a third party in the relationship established by the clinical interview. Finally, DP risks defining mental health goals and dictating the means of attaining them (Figure 2). Illnesses may become political, part of a biopower seeking social control [135-137]. The term mental disorder used in the DSM-5 (instead of illness or disease) speaks for itself, the implicit meaning being that good mental health corresponds to a supposed mental order: illnesses render individuals unfit and remove their autonomy, such that they are dependent on the quantified self or on medical authorities (specialists) with increasingly narrow areas of expertise. Indeed, the current trend in psychiatry is to develop expert centers in which patients undergo a single clinical assessment, resulting in fast diagnosis and therapeutic guidelines. This paternalistic psychiatric assessment has little to do with the patient’s own expertise, which only HCPs who provide follow-up and long-term care can fully know. The risk is that HCPs relying on this data set will reach their diagnosis too early, interfering in the treatment procedure and compromising the follow-up needed to cure psychiatric illnesses.

The concerns of patients whose intrinsic ability to normalize their environment is impaired are regarded as lying on the border between the normal and the pathological. DP could shift these borders by entering the patient-HCP relationship with a third party (Figure 2) or a fourth party if the relationship includes a family member or trusted person [138]. It raises the issue of responsibility, which also concerns the DP designer, manager, or analyst. Technological tools are not autonomous. DP threatens to go against the principles governing new technologies, namely neutrality, diversity, transparency, equity, loyalty, and overall comprehensibility.

Equity has been identified as one of the main ethical challenges of digital health [139]. Indeed, algorithms are constructed on complex models with user interfaces that are often difficult to understand. If users are not trained, DP will be misused due to the learning bias. HCPs and users could be victims of the black box effect, which can hinder the traceability of the decision-making process. It may well exacerbate the existing inequality of access to psychiatric care, penalizing patients from underprivileged backgrounds [94], on the wrong side of the digital divide [140], or exposed to the stigma [141] that extensively affects the psychiatric population [142]. Studies on the use of health data also tend to show that they are more acceptable to younger psychiatric patients [143], and adherence is predicted by a higher education level in schizophrenia [144]. There is therefore a risk that these technological advances will only benefit individuals who already enjoy easier access to psychiatric care.

In psychiatry, respect for privacy and confidentiality is also an essential part of the medical principle of patient autonomy, but health digitalization has brought about a paradigm shift. The sharing of information between different HCPs is now considered beneficial for patients. Professional integrity is replacing confidentiality [145,146]. Data anonymization can easily be bypassed by cross-referencing. Thus, personal health threatens to move from the private to the public arena [139] or to be extensively traded by private companies, particularly in psychiatry, where intimate data are of utmost importance for clinical practice. DP has indeed the potential for intrusion that would go against medical confidentiality, with questioning about the patients’ consent to share their health data. Hence, informed consent and data ownership are among the main issues [95,139,147].

Phenomenology currently lies at the core of mental health but risks being dislodged. Good health may contain a moral imperative, with pathologies being attributed either to alienation from the environment (nurture) or to biological predispositions (nature). When patients are freed of all responsibility in this way, failure ceases to exist and is replaced by illnesses, as has already happened with learning disorders, substance use disorders, and other deviances, ultimately leading to uneasiness [148]. Indeed, DP could stimulate nudging strategies, defined as “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives” [149]. Nudges could create artificial health but also artificial illnesses, compromising patients’ autonomy [150,151] and agency [152]. Thus, it could be a barrier to meeting their mental health needs while creating new ones. The drift toward normativity through the medicalization of society can already be observed with the cult of performance [153] and the illusion of omnipotence over illness and death. Based on the notion of personal development, the post-Freudian therapies that first emerged back in the 1960s are intended to enable the immediate gratification of drives, thus increasing the isolation of the self [154]. The ultimate goal is for individuals to fulfill their personal achievement goals by worshiping authenticity and assertiveness, thereby exacerbating the very symptoms these therapies claim to cure. By further increasing the emphasis on performance, DP could contribute to the loss of identification with generational continuity, in other words, the inability of individuals to gradually identify themselves with the wellness and success of others rather than their own and, with the fear of growing old, the inability to think about their posterity and the handing of the baton to the next generation. The COVID-19 pandemic may be a good example of the counterhistorical health trade-off between generations: the sacrifice of liberty on behalf of health, with the collateral damage of poorer mental health among young people [155,156].

More and more digital mental health apps are available in web-based sales spaces. However, their design is seldom inspired by rigorous scientific research [105]. The popularity of these apps shows that there is consumer demand. The same need manifests itself when patients turn to complementary medicine because their physician fails to provide satisfaction. We can assume that through their use of these apps, individuals are not only responding to various marketing ploys but are also seeking to improve their daily lives. The COVID-19 pandemic undoubtedly fostered this change in the relationship between individuals and connected health, but there is a dearth of qualitative research on the place that connected objects now have in people’s daily lives [118].

DP gives psychiatry an opportunity to adopt more modern ways of helping patients. However, it raises crucial questions about the values underpinning the definition of mental health [157] and could profoundly undermine the “sacred trust” that patients place in their physician to understand their illness. All in all, interdisciplinary collaboration in DP research is necessary [25,158], fostering expertise in psychiatry, computer science, data science, innovation, ethics, law, and the social sciences, to ensure the development of robust and clinically meaningful DP tools [159]. Further recommendations are needed to ensure that there is a true revolution in mental health.